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Found 10 papers, 2 papers with code
A Discriminative Single-Shot Segmentation Network for Visual Object Tracking
D3S2 outperforms the leading segmentation tracker SiamMask on video object segmentation benchmarks and performs on par with top video object segmentation algorithms.
DAL -- A Deep Depth-aware Long-term Tracker
In this work, we propose a deep depth-aware long-term tracker that achieves state-of-the-art RGBD tracking performance and is fast to run.
D3S -- A Discriminative Single Shot Segmentation Tracker
D3S outperforms the leading segmentation tracker SiamMask on video object segmentation benchmark and performs on par with top video object segmentation algorithms, while running an order of magnitude faster, close to real-time.
CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark
A long-term visual object tracking performance evaluation methodology and a benchmark are proposed.
Performance Evaluation Methodology for Long-Term Visual Object Tracking
A long-term visual object tracking performance evaluation methodology and a benchmark are proposed.
Now you see me: evaluating performance in long-term visual tracking
We propose a new long-term tracking performance evaluation methodology and present a new challenging dataset of carefully selected sequences with many target disappearances.
FuCoLoT -- A Fully-Correlational Long-Term Tracker
We propose FuCoLoT -- a Fully Correlational Long-term Tracker.
Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking
Object-to-camera motion produces a variety of apparent motion patterns that significantly affect performance of short-term visual trackers.
Discriminative Correlation Filter with Channel and Spatial Reliability
Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance.
Ranked #13 on
Visual Object Tracking
on VOT2017/18
(using extra training data)
Deformable Parts Correlation Filters for Robust Visual Tracking
Deformable parts models show a great potential in tracking by principally addressing non-rigid object deformations and self occlusions, but according to recent benchmarks, they often lag behind the holistic approaches.
